{"title":"流处理系统的不确定性感知弹性虚拟机调度","authors":"Shigeru Imai, S. Patterson, Carlos A. Varela","doi":"10.1109/CCGRID.2018.00021","DOIUrl":null,"url":null,"abstract":"Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a scheduling framework that incorporates three statistical techniques to improve Quality of Service (QoS) of cloud stream processing systems: (i) uncertainty quantification to consider variance in the MST model; (ii) online learning to update MST model as new performance metrics are gathered; and (iii) workload models to predict input data stream rates assuming regular patterns occur over time. Our framework can be parameterized by a QoS satisfaction target that statistically finds the best performance/cost tradeoff. Our results illustrate that each of the three techniques alone significantly improves QoS, from 52% to 73-81% QoS satisfaction rates on average for eight benchmark applications. Furthermore, applying all three techniques allows us to reach 98.62% QoS satisfaction rate with a cost less than twice the cost of the optimal (in hindsight) VM allocations, and half of the cost of allocating VMs for the peak demand in the workload.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Uncertainty-Aware Elastic Virtual Machine Scheduling for Stream Processing Systems\",\"authors\":\"Shigeru Imai, S. Patterson, Carlos A. Varela\",\"doi\":\"10.1109/CCGRID.2018.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a scheduling framework that incorporates three statistical techniques to improve Quality of Service (QoS) of cloud stream processing systems: (i) uncertainty quantification to consider variance in the MST model; (ii) online learning to update MST model as new performance metrics are gathered; and (iii) workload models to predict input data stream rates assuming regular patterns occur over time. Our framework can be parameterized by a QoS satisfaction target that statistically finds the best performance/cost tradeoff. Our results illustrate that each of the three techniques alone significantly improves QoS, from 52% to 73-81% QoS satisfaction rates on average for eight benchmark applications. Furthermore, applying all three techniques allows us to reach 98.62% QoS satisfaction rate with a cost less than twice the cost of the optimal (in hindsight) VM allocations, and half of the cost of allocating VMs for the peak demand in the workload.\",\"PeriodicalId\":321027,\"journal\":{\"name\":\"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCGRID.2018.00021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGRID.2018.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Uncertainty-Aware Elastic Virtual Machine Scheduling for Stream Processing Systems
Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a scheduling framework that incorporates three statistical techniques to improve Quality of Service (QoS) of cloud stream processing systems: (i) uncertainty quantification to consider variance in the MST model; (ii) online learning to update MST model as new performance metrics are gathered; and (iii) workload models to predict input data stream rates assuming regular patterns occur over time. Our framework can be parameterized by a QoS satisfaction target that statistically finds the best performance/cost tradeoff. Our results illustrate that each of the three techniques alone significantly improves QoS, from 52% to 73-81% QoS satisfaction rates on average for eight benchmark applications. Furthermore, applying all three techniques allows us to reach 98.62% QoS satisfaction rate with a cost less than twice the cost of the optimal (in hindsight) VM allocations, and half of the cost of allocating VMs for the peak demand in the workload.